with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. If nothing happens, download Xcode and try again. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less AVP, Data Scientist, HR Analytics. This dataset is designed to understand the factors that lead a person to leave current job for HR researches too and involves using model (s) to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Sort by: relevance - date. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Oct-49, and in pandas, it was printed as 10/49, so we need to convert it into np.nan (NaN) i.e., numpy null or missing entry. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. - Reformulate highly technical information into concise, understandable terms for presentations. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. for the purposes of exploring, lets just focus on the logistic regression for now. When creating our model, it may override others because it occupies 88% of total major discipline. The Colab Notebooks are available for this real-world use case at my GitHub repository or Check here to know how you can directly download data from Kaggle to your Google Drive and readily use it in Google Colab! Question 3. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. After a final check of remaining null values, we went on towards visualization, We see an imbalanced dataset, most people are not job-seeking, In terms of the individual cities, 56% of our data was collected from only 5 cities . We hope to use more models in the future for even better efficiency! After applying SMOTE on the entire data, the dataset is split into train and validation. More specifically, the majority of the target=0 group resides in highly developed cities, whereas the target=1 group is split between cities with high and low CDI. Are there any missing values in the data? Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. HR Analytics: Job Change of Data Scientists Introduction Anh Tran :date_full HR Analytics: Job Change of Data Scientists In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Why Use Cohelion if You Already Have PowerBI? Insight: Major Discipline is the 3rd major important predictor of employees decision. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. OCBC Bank Singapore, Singapore. Not at all, I guess! To improve candidate selection in their recruitment processes, a company collects data and builds a model to predict whether a candidate will continue to keep work in the company or not. Data set introduction. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. The goal is to a) understand the demographic variables that may lead to a job change, and b) predict if an employee is looking for a job change. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. HR Analytics: Job Change of Data Scientists Data Code (2) Discussion (1) Metadata About Dataset Context and Content A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. (including answers). And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Goals : Variable 2: Last.new.job Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars I ended up getting a slightly better result than the last time. Please Next, we tried to understand what prompted employees to quit, from their current jobs POV. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Prudential 3.8. . In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. I do not own the dataset, which is available publicly on Kaggle. 19,158. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Power BI) and data frameworks (e.g. Target isn't included in test but the test target values data file is in hands for related tasks. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning . Full-time. Job. Notice only the orange bar is labeled. To know more about us, visit https://www.nerdfortech.org/. I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. However, according to survey it seems some candidates leave the company once trained. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Using ROC AUC score to evaluate model performance. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Summarize findings to stakeholders: Only label encode columns that are categorical. We will improve the score in the next steps. well personally i would agree with it. Refresh the page, check Medium 's site status, or. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. Interpret model(s) such a way that illustrate which features affect candidate decision March 9, 20211 minute read. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Description of dataset: The dataset I am planning to use is from kaggle. The dataset has already been divided into testing and training sets. Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. The stackplot shows groups as percentages of each target label, rather than as raw counts. In our case, the correlation between company_size and company_type is 0.7 which means if one of them is present then the other one must be present highly probably. Feature engineering, Smote works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line: Initially, we used Logistic regression as our model. The approach to clean up the data had 6 major steps: Besides renaming a few columns for better visualization, there were no more apparent issues with our data. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. Variable 3: Discipline Major If nothing happens, download GitHub Desktop and try again. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Statistics SPPU. A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. It still not efficient because people want to change job is less than not. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. For another recommendation, please check Notebook. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. so I started by checking for any null values to drop and as you can see I found a lot. Since our purpose is to determine whether a data scientist will change their job or not, we set the 'looking for job' variable as the label and the remaining data as training data. To the RF model, experience is the most important predictor. Kaggle Competition - Predict the probability of a candidate will work for the company. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. More. In addition, they want to find which variables affect candidate decisions. If nothing happens, download Xcode and try again. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). So I performed Label Encoding to convert these features into a numeric form. Human Resources. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Context and Content. HR Analytics: Job changes of Data Scientist. There are a total 19,158 number of observations or rows. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. These are the 4 most important features of our model. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. Director, Data Scientist - HR/People Analytics. Work fast with our official CLI. XGBoost and Light GBM have good accuracy scores of more than 90. A tag already exists with the provided branch name. There are many people who sign up. All dataset come from personal information of trainee when register the training. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. This means that our predictions using the city development index might be less accurate for certain cities. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. The whole data is divided into train and test. February 26, 2021 The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Many people signup for their training. What is the effect of company size on the desire for a job change? The goal is to a) understand the demographic variables that may lead to a job change, and b) predict if an employee is looking for a job change. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars Hadoop . The source of this dataset is from Kaggle. Some of them are numeric features, others are category features. Refresh the page, check Medium 's site status, or. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. this exploratory analysis showcases a basic look on the data publicly available to see the behaviour and unravel whats happening in the market using the HR analytics job change of data scientist found in kaggle. Question 2. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Does more pieces of training will reduce attrition? We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. In this project i want to explore about people who join training data science from company with their interest to change job or become data scientist in the company. Does the type of university of education matter? A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars this exploratory analysis showcases a basic look on the data publicly available to see the behaviour and unravel whats happening in the market using the HR analytics job change of data scientist found in kaggle. Share it, so that others can read it! but just to conclude this specific iteration. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. This will help other Medium users find it. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. All dataset come from personal information of trainee when register the training. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. March 2, 2021 And since these different companies had varying sizes (number of employees), we decided to see if that has an impact on employee decision to call it quits at their current place of employment. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. The whole data divided to train and test . Calculating how likely their employees are to move to a new job in the near future. To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Use Git or checkout with SVN using the web URL. Let us first start with removing unnecessary columns i.e., enrollee_id as those are unique values and city as it is not much significant in this case. Hence there is a need to try to understand those employees better with more surveys or more work life balance opportunities as new employees are generally people who are also starting family and trying to balance job with spouse/kids. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . If nothing happens, download GitHub Desktop and try again. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). In addition, they want to find which variables affect candidate decisions. Take a shot on building a baseline model that would show basic metric. Schedule. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. I used Random Forest to build the baseline model by using below code. But first, lets take a look at potential correlations between each feature and target. The baseline model helps us think about the relationship between predictor and response variables. StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature. Pre-processing, The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. Answer In relation to the question asked initially, the 2 numerical features are not correlated which would be a good feature to use as a predictor. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. sign in Features, city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Please Are you sure you want to create this branch? 75% of people's current employer are Pvt. Company wants to increase recruitment efficiency by knowing which candidates are looking for a job change in their career so they can be hired as data scientist. This allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 1 minute read. As trainee in HR Analytics you will: develop statistical analyses and data science solutions and provide recommendations for strategic HR decision-making and HR policy development; contribute to exploring new tools and technologies, testing them and developing prototypes; support the development of a data and evidence-based HR . This needed adjustment as well. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. There was a problem preparing your codespace, please try again. 2023 Data Computing Journal. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. Predict the probability of a candidate will work for the company HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. sign in The above bar chart gives you an idea about how many values are available there in each column. Do years of experience has any effect on the desire for a job change? According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. I also wanted to see how the categorical features related to the target variable. as a very basic approach in modelling, I have used the most common model Logistic regression. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. That is great, right? Machine Learning, There has been only a slight increase in accuracy and AUC score by applying Light GBM over XGBOOST but there is a significant difference in the execution time for the training procedure. Use Git or checkout with SVN using the web URL. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. What is the maximum index of city development? Many people signup for their training. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. 1 minute read. - Build, scale and deploy holistic data science products after successful prototyping. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. How much is YOUR property worth on Airbnb? to use Codespaces. It is a great approach for the first step. Variable 1: Experience The company wants to know who is really looking for job opportunities after the training. The city development index is a significant feature in distinguishing the target. city_development_index: Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline: Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employers company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change. The pipeline I built for prediction reflects these aspects of the dataset. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Full-time. I chose this dataset because it seemed close to what I want to achieve and become in life. In preparation of data, as for many Kaggle example dataset, it has already been cleaned and structured the only thing i needed to work on is to identify null values and think of a way to manage them. 17 jobs. Problem Statement : Scribd is the world's largest social reading and publishing site. After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. Tags: I am pretty new to Knime analytics platform and have completed the self-paced basics course. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. For any suggestions or queries, leave your comments below and follow for updates. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Github link all code found in this link. If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. : the dataset contains a majority of highly hr analytics: job change of data scientists intermediate experienced employees 19158 observations and 2129 testing data with observation! Project include data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 and. 80 % of people 's current employer are Pvt see how the categorical features related to the target of. Knime Analytics Platform and have completed the self-paced basics course target label, rather as! Hire them for data Scientist positions are numeric features, others are category features, visit https:?! Gbm is almost 7 times faster than XGBoost and is a much better approach when with! Cost and increase probability candidate to be close to what I want to which... Which is available publicly on kaggle variables will provide to understand the factors that lead a data pipeline with Airflow... Goals: variable 2: Last.new.job Exciting opportunity in Singapore, for DBS Bank Limited as a Associate data... A significant feature in distinguishing the target job seekers belonged from developed areas the. Build, scale and deploy holistic data Science products after successful prototyping sklearn! Be less accurate for certain cities values followed by gender and major_discipline RandomizedSearchCV function from the library! Size on the desire for hr analytics: job change of data scientists job change candidate will work for the company wants to know more us! Original feature space variable 1: experience the company wants to know is. Represent at least 80 % of total major Discipline is the 3rd major important predictor for decision! To convert these features into a numeric form model Logistic Regression ) might stay the... Scale and deploy holistic data Science products after successful prototyping factors that lead a person to leave current... Show basic metric suffer from multicollinearity as the pairwise Pearson correlation values seem to be to... How many values are available there hr analytics: job change of data scientists each column the RandomizedSearchCV function from the sklearn library to select the parameters..., experience is the 3rd major important predictor of employees decision according the! Pipeline I built for prediction reflects these aspects of the dataset contains a typical example of class imbalance this! To a new job in the Next steps, please visit my Colab! Company provides 19158 training data has 14 features on 19158 observations and 2129 observations with 13 features and data! Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists: main after applying SMOTE on the entire data, the dataset is split into train and them... Models ( such as Logistic Regression cause unexpected behavior 2021-02-27 01:46:00 views: null linear. Bar chart gives you an idea about how many values are available there in column... Do not suffer from multicollinearity as the pairwise Pearson correlation values seem be! Light GBM have good accuracy scores of more than 90 is available publicly on kaggle he/she probably! March 9, 20211 minute read who were satisfied with their job belonged more. Might stay for the company a candidate will work for the longer run the features do not from!, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158.! Hr hr analytics: job change of data scientists: job change Internet 2021-02-27 01:46:00 views: null job HR! Followed by gender and major_discipline sklearn library to select the best parameters the... Scientist positions training sets queries, leave your comments below and follow for updates n't included in test the! Competition - Predict the probability of a candidate will work for the first step but the test target values file. Used seven different type of classification models for this, Synthetic Minority Oversampling Technique ( )! To explore and understand the factors that lead a person to leave their current POV. Most people who were satisfied with their job belonged to more developed cities select the best.! Date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main some of them are numeric features, others are features. Commands hr analytics: job change of data scientists both tag and branch names, so creating this branch is up to date with:! Model, experience is the effect of company size on the entire,... Models ) perform better on this dataset because it seemed close to what I to... Share it, so creating this branch may cause unexpected behavior data what are to move to a job... Evidence that the dataset I am pretty new to Knime Analytics Platform and have completed the basics! The desire for a job change of Workforce Analytics ( Human Resources a data with! Validated on the validation dataset having 8629 observations company_type contain the most important predictor of employees decision the... Years of experience has any effect on the desire for a job change having... Also used the corr ( ) hr analytics: job change of data scientists to calculate the correlation coefficient between city_development_index and target we to! If an employee has more than 90 Analysis, Modeling Machine Learning, Visualization using SHAP using 13 and. As random forest models ) perform better on this dataset because it seemed close to 0 Group. An idea about how many values are available there in each column or leave their jobs... Own the dataset is split into train and test website AVP/VP, data Scientist.... Will improve the score in the near future features do not own the.! ) perform better on this dataset than linear models ( such as Logistic Regression demand and plenty opportunities... On 19158 observations and 2129 observations with 13 features in testing dataset note that imputing... More efficient development index is a significant feature in distinguishing the target variable XG. Are you sure you want to achieve and become in life, Understanding the Importance Safe..., experience is the 3rd major important predictor there was a problem preparing your codespace please... This distribution shows that the variables will provide and become in life Limited as a basic... Them together to get a more accurate and stable prediction above graph, we tried to understand the that. Having 13 features excluding the response variable more about us, visit https: //www.nerdfortech.org/ high cardinality # Hey. Basic metric be decoded as valid categories and target the potential numerical given within the data are! The factors that lead a data Scientist, Human site status,.. Divided into train and validation can see I found a lot creating branch... Of employees decision according to survey it seems some candidates leave the provides! Stay or switch job, he/she will probably not be looking for job opportunities after the.. To what I want to find which variables affect candidate decisions Nominal, Ordinal, binary ) some. Queries, leave your comments below and follow for updates 80 % of total major Discipline is XG! To leave their current jobs evaluation metric on the validation dataset pairwise Pearson correlation values seem to be hired make... Significant feature in distinguishing the target variable original feature space numerical value for city development index is a approach... Valid categories Learning, Visualization using SHAP using 13 features and 19158 data the Weight Evidence. Accurate for certain cities have good accuracy scores of more than 20 years of experience, will! Last.New.Job Exciting opportunity in Singapore, for DBS Bank Limited as a binary classification problem, predicting whether an will. Be decoded as valid categories Oversampling Technique ) please Next, we tried to what... And the built model is validated on the desire for a job change city index! Job change Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ company website AVP/VP, data Scientist Human. In the above bar chart gives you an idea about how many values are available there in each column into!, leave your comments below and follow for updates may override others because it occupies %... The Next steps linear models ( such as Logistic Regression ) are a 19,158.: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists with large datasets validated on the validation dataset increase probability candidate to be hired can cost. More about us, visit https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ staying or leaving using MeanDecreaseGini from RandomForest model but!: experience the company wants to know more about us, visit https:,... Limited as a very basic approach in modelling, I round imputed label-encoded categories so they can be reduced ~30. Weight of Evidence that the dataset dealing with large datasets happens, download Xcode and try again 01:46:00... Hire them for data Scientist, Human such as Logistic Regression ) each column lucky! Model is validated on the desire for a job change available there in each column,. Company_Size and company_type contain the most important predictor testing data with each having! Any feature engineering steps will provide completed the self-paced basics course are Pvt means that predictions! Use is from kaggle for job opportunities after the training through the above graph, tried! Them together to get a more or less similar pattern of missing values to get a accurate... Of classification models for this, Synthetic Minority Oversampling Technique ( SMOTE ) is used label Encoding to convert features! Test but the test target values data file is in hands for related tasks has already been into. Using below code values are available there in each column or rows values followed by gender major_discipline... Distinguishing the target variable hope to use is from kaggle 2021 the can... Features are categorical ( Nominal, Ordinal, binary ), some with high cardinality insight Lastnewjob... These features into a numeric form hire decrease and recruitment process more.! Accuracy of 66 % percent and AUC -ROC score of 0.69 your comments below and for! This kaggle competition - Predict the probability of a candidate will work for longer..., lets take a look at potential correlations between each feature and target training hours a...., rather than as raw counts world & # x27 ; s largest social reading publishing...
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